“What is the true value of our data and AI initiatives?”

Too often, we drive all our energy into tools, processes, and outputs, but forget to ask ourselves how what we build actually makes a difference. For enterprises, this means looking beyond AI models and dashboards to see how our data drives real, measurable impact. Understanding the difference between output and outcome is what separates activity from transformation.

In this episode of Don’t Panic, it's Just Data, host Doug Laney and Nadiem von Heydebrand, CEO and Co-founder of Mindfuel, explore how organisations can turn data and AI efforts into actionable business outcomes. They discuss the concept of the “value layer”, a framework connecting data initiatives to business needs, emphasising the importance of understanding business problems before developing solutions.

Nadiem stresses that prioritising initiatives and fostering strong collaboration between business and data teams are critical to unlocking maximum value from data and AI efforts.

Why Data and AI Impact Management Matters

Many organisations are investing heavily in data and AI, but turning these investments into real business value remains a challenge. This is because a critical gap exists between technical execution and business outcomes. Data and AI teams work on initiatives without first clarifying what business problems they're solving or how success will be measured.

Data and AI Impact Management bridges this gap by establishing the “value layer" between business strategy and technical platforms. This approach starts with structured demand management for use cases, enables systematic prioritisation based on actual value potential, and tracks initiatives throughout their lifecycle to ensure they deliver impact against business goals. This shift, from building solutions in search of problems to solving qualified business problems with purpose-built solutions, transforms data and AI teams from technical support functions into strategic partners who deliver value, stronger strategic alignment, and lasting competitive advantage.

Nadiem says, “Applying a product mindset within data initiatives is key, and it's the foundational effort to be able to drive value.”

He also notes that not every use case delivers direct financial impact, and the value layer helps clarify demand, manage use cases effectively, and uncover each initiative’s business value

For more insights  and solutions, visit Mindfuel

Takeaways

  • Organisations struggle to connect data initiatives to business outcomes.
  • The value layer is essential for linking data to business demands.
  • Understanding the actual business problem is crucial for success.
  • Value management encompasses the entire lifecycle of initiatives.
  • A product mindset helps focus on outcomes rather than outputs.
  • Not all data use cases have direct dollar values.
  • Data and AI impact management creates transparency for data teams.
  • Establishing a product mindset is key for data products.
  • Connecting processes to the operating model enhances effectiveness.
  • Collaboration between business and data teams is vital for unlocking value.

Chapters

00:31 Introduction: Don't Panic, It's Just Data

01:37 The Missing Piece: Introducing the Value Layer

07:11 Value Management Lifecycle

10:46 Product Mindset in Data Initiatives

14:10 Distinguishing Value and Impact

17:04 Impact Management and Investment Justification

19:34 Mindfuel's Three-Step Guide to Impact Management

21:00 Conclusion and Key Takeaways